OMASCED-SABIA

Work Plan

The project is organized into eight interconnected Work Packages covering conceptual modelling, data, optimization, Artificial Intelligence, software development, validation, coordination and dissemination.

36 months · 8 Work Packages

OVERVIEW

From Conceptual Modelling to Real-world Validation

The OMASCED-SABIA Work Plan follows a progressive methodology in which conceptual and data foundations support the development of advanced optimization and Artificial Intelligence methods, which are then integrated into a Decision Support System and validated through representative Field Service scenarios.

The first stages of the project focus on understanding Field Service planning processes, defining the decision framework and identifying the information and data required to support advanced planning and scheduling.

Subsequent stages develop optimization models and intelligent algorithms, integrate them into a software platform and evaluate the resulting solutions through computational experiments and pilot applications.

METHODOLOGY

Five Complementary Development Levels

1. Conceptual Modelling

Definition of the Field Service planning framework, decision processes, actors, constraints and objectives.

2. Data Architecture and Governance

Identification, structuring and management of heterogeneous data required for adaptive and intelligent decision-making.

3. OR + AI Development

Development of mathematical models, metaheuristics and Deep Reinforcement Learning approaches for multi-objective planning and scheduling.

4. Decision Support System

Integration of models, algorithms and real-time data into an advanced software environment for operational decision support.

5. Validation and Pilots

Experimental evaluation of the proposed approaches and validation through representative Field Service use cases.

WORK PACKAGES

Eight Work Packages

WP1

Conceptual Framework

Definition of the conceptual framework, scope and structure of Field Service planning and scheduling problems in dynamic environments.

WP2

Planning Processes

Analysis of planning processes, decision levels, operational constraints and information flows involved in Field Service management.

WP3

Data Management and Governance

Definition of data sources, data structures, integration mechanisms and governance principles required to support intelligent planning.

WP4

Advanced Solutions: Review and Analysis

Analysis of existing Operations Research and Artificial Intelligence approaches applicable to Field Service planning and scheduling.

WP5

Advanced and Intelligent Solutions

Development of multi-objective models, metaheuristics and Deep Reinforcement Learning algorithms for adaptive decision-making.

WP6

Software System Development

Design and implementation of the advanced Decision Support System integrating data, models, algorithms and operational information.

WP7

Validation and Pilots

Experimental validation of the proposed methods and evaluation of the Decision Support System through representative pilot scenarios.

WP8

Coordination and Dissemination

Scientific and technical coordination of the project, dissemination of results and promotion of knowledge transfer to companies, researchers and society.

PROJECT LOGIC

A Progressive Research Process

WP1

Understand

Establish the conceptual basis and define the Field Service planning problem.

WP2–3

Model

Analyse processes and structure the information required for intelligent decision-making.

WP4–5

Optimize

Develop mathematical models and advanced OR + AI solution methods.

WP6–7

Implement and Validate

Integrate the developed methods into a Decision Support System and validate them in representative scenarios.

EXPECTED OUTCOMES

Main Scientific and Technological Results

Conceptual Framework

A comprehensive framework for understanding and modelling dynamic Field Service planning and scheduling.

OR + AI Models

Multi-objective mathematical models and advanced algorithms combining Operations Research, metaheuristics and Artificial Intelligence.

Decision Support System

An advanced DSS integrating heterogeneous data, optimization models and intelligent algorithms.

Experimental Validation

Scientific validation of the proposed solutions through computational experimentation and representative pilot applications.

MILESTONES

Key Project Milestones

Conceptual Framework and Modelling

Consolidation of the conceptual framework and modelling foundations for Field Service planning.

Unified Data Model and Governance

Definition of the unified data structure and governance principles required by the project.

OR + AI Prototypes

Development of the first integrated prototypes combining optimization and Artificial Intelligence methods.

Integrated DSS Beta

Availability of a beta version of the integrated Decision Support System.

Final Validation

Final validation of the developed solutions through one or two representative pilot applications.

36-month roadmap

Project Activity Timeline

Planned execution of the eight Work Packages across three project years. The schedule follows the approved project proposal.

Work Package

Year 1

Year 2

Year 3

Activity

M1–3

M4–6

M7–9

M10–12

M13–15

M16–18

M19–21

M22–24

M25–27

M28–30

M31–33

M34–36

WP1Conceptual framework

WP2Planning processes

WP3Data governance

WP4Review of advanced solutions

WP5Advanced OR + AI models

WP6DSS / software development

WP7Validation and pilots

WP8Management & dissemination

Main execution Partial execution / integration

Key milestones

Conceptual framework and process modelling

Unified data model and data governance

OR + AI functional prototypes

Integrated DSS beta

Final validation and impact analysis

Timeline shown in project months, not calendar dates. Based on the Work Plan schedule in the project proposal (pp. 11–12).

Meet the Research Team

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